Discovery of a new marker to identify myeloid cells associated with metastatic breast tumours

Background Myeloid cells play an essential role in cancer metastasis. The phenotypic diversity of these cells during cancer development has attracted great interest; however, their functional heterogeneity and plasticity have limited their role as prognostic markers and therapeutic targets. Methods To identify markers associated with myeloid cells in metastatic tumours, we compared transcriptomic data from immune cells sorted from metastatic and non-metastatic mammary tumours grown in BALB/cJ mice. To assess the translational relevance of our in vivo findings, we assessed human breast cancer biopsies and evaluated the association between arginase 1 protein expression in breast cancer tissues with tumour characteristics and patient outcomes. Results Among the differentially expressed genes, arginase 1 (ARG1) showed a unique expression pattern in tumour-infiltrating myeloid cells that correlated with the metastatic capacity of the tumour. Even though ARG1-positive cells were found almost exclusively inside the metastatic tumour, ARG1 protein was also present in the plasma. In human breast cancer biopsies, the presence of ARG1-positive cells was strongly correlated with high-grade proliferating tumours, poor prognosis, and low survival. Conclusion Our findings highlight the potential use of ARG1-positive myeloid cells as an independent prognostic marker to evaluate the risk of metastasis in breast cancer patients. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12935-023-03136-w.


FIGURES
was used to exclude debris, followed by a forward scatter height (FSC-H)/ forward scatter area (FSC-A) to exclude doublets and Zombie Aqua (live dead marker)/ FSC to exclude dead cells.A) Gating strategy to identify ARG1 positive cells in immune and non-immune populations.The single and live cells were plotted for ARG1 vs CD45 (immune cell marker) to identify if immune (CD45+) or nonimmune (CD45-) cells were the primary source of ARG1.B) Gating strategy to identify ARG1-positive immune cells.Single/live cells were plotted for CD45 vs FSC to distinguish between immune (CD45+) and non-immune (CD45-) cells.Further, the immune cells were plotted for ARG1 vs CD11b (myeloid cell marker) to identify if myeloid (CD11b+) or lymphoid (CD11b-) cells were the primary ARG1 source.
FMOs were used to gate positive populations.

Figure
Figure S1: A) Principal component analysis (PCA) of the RNA-sequencing of CD45+ and CD45sorted cells from 67NR and 66cl4 tumours (n=5).B) Volcano plot of 67NR CD45+ cells vs 66cl4 CD45+ cells showing the differential genes expression (n=5).C) Gene ontology Molecular function analysis of the top 500 overexpressed genes in 66cl4 CD45+ indicates a significant association of these genes with CXC chemokine receptor activity and cytokines binding.D) Gene ontology Cellular

Figure S2 :
Figure S2: Arginase gene and protein expression at 2.5X and single cells at 400X.Panels A-D show a representative image of in situ hybridisation (A-B) and immunohistochemistry (C-D).The spatial distribution of ARG1 gene and protein in 67NR and 66cl4 tumours is shown at 2.5X magnification.It shows the difference in ARG1 mRNA and protein abundance and distribution in 67NR and 66cl4 tumours.Panels E-F show a representative image of ARG1 protein by IHC at 400X magnification, and the panel to the right shows a zoom-in of the ARG1 positive cell.

Figure
FigureS3shows flow cytometry gating strategies on dissociated tumours and organs to identify ARG1-positive cells.To investigate the source of ARG1 in 67NR and 66cl4 tumours, antibodies mentioned in Materials and Methods were used.Forward/ side angle light scatter (FSC-A/SSC-A)

Figure
Figure S4 shows flow cytometry gating strategies on dissociated tumours/organs to identify immune subpopulations.Gating strategy to assess myeloid cell subpopulations in tumours.The resulting population of single, live cells were plotted for CD45 (immune cell marker) to distinguish between immune (CD45+) and non-immune (CD45-) cells.Immune cells were plotted for CD11b vs Ly6G (myeloid cell marker/neutrophil marker) to identify different immune cell populations.The plot revealed three distinct CD11b+ cell populations of interest: 1 subset of CD11b+/ Ly6G-and 2 population subsets of CD11b+/Ly6G+ cells.The two CD11b+/Ly6G+ cell populations could be distinguished based on the level of Ly6G as cells with intermediate or high Ly6G expression.These populations were labelled CD11b+/Ly6G-, CD11b+/ Ly6G intermediate and CD11b+/Ly6G high.

Figure
FigureS6shows flow cytometry gating strategies on dissociated tumours and organs to identify ARG1+ immune subpopulations.Gating approach to identify ARG1 source in CD11b+ subsets.Single, live cells were plotted for CD45 (immune cell marker), followed by CD11b (myeloid cell marker).The resulting myeloid cells were plotted for ARG1 vs Ly6G (neutrophils marker) to identify the CD45+/CD11b+ population source of ARG1.FMOs were used to gate positive populations.

Figure
Figure S8 shows ARG1 protein quantification in plasma by ELISA.Bars represent means ± SEM, and each data point represents a single animal (Control n: 10, 67NR n:14; 66cl4 n:14).Statistical significance was determined using one-way ANOVA followed by Kruskal-Wallis test multiple comparisons.** p<0.005.

Table S1 Gene
TABLES Ontology analysis of Biological Process elevated in 66cl4 CD45+ cells compared with 67NR CD45+ cells.

Table S2 :
Fn1, Tnbs1 and Arg1 expression levels as transcripts per million in the CD45+ and CD45cells isolated from 67NR and 66cl4 tumours.

Table S4 :
Parameter estimates for the log-normal accelerated failure time model, unadjusted and adjusted for other variables.